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λ - Universiti Utara · PDF fileClassification, clustering and ... The stocks that are listed on the plantation sector of Bursa Malaysia, the total number of active plantation stocks

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  • 63

    Journal of ICT, 15, No. 2 (December) 2016, pp: 6384

    23

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    EVALUATION ON RAPID PROFILING WITH CLUSTERING ALGORITHMS FOR PLANTATION STOCKS

    ON BURSA MALAYSIA

    Keng-Hoong Ng & Kok-Chin KhorMultimedia University, Malaysia

    [email protected]; [email protected]

    ABSTRACT

    Building a stock portfolio often requires extensive financial knowledge and Herculean efforts looking at the amount of financial data to analyse. In this study, we utilized Expectation Maximization (EM), K-Means (KM), and Hierarchical Clustering (HC) algorithms to cluster the 38 plantation stocks listed on Bursa Malaysia using 14 financial ratios derived from the fundamental analysis. The clustering allows investors to profile each resulted cluster statistically and assists them in selecting stocks for their stock portfolios rapidly. The performance of each cluster was then assessed using 1-year stock price movement. The result showed that a cluster resulted from EM had a better profile and obtained a higher average capital gain as compared with the other clusters.

    Keywords: Stock profiling, stock portfolio, financial ratios, expectation maximization, K-means, hierarchical clustering.

    INTRODUCTION

    Investing in stock markets is not an easy task for many people as stock markets are complex and dynamic systems. Short term movements or patterns in stock markets are always unpredictable and difficult to trace. Thus, lucrative returns are difficult to gain from stock investments. However, investors and financial researchers still keen to adopt different approaches to understand the behaviour of stock markets. As a result, the research into stock markets remains interesting and appealing to them. In the early years, research on the stock price movements and predictions was primarily based on statistical

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    approaches (Brown & Warner, 1985; Pearce, 1984). But in recent years, the focus of the stock research has been shifted to applying data mining techniques (Ou & Wang, 2009).

    Data mining is a process of identifying interesting patterns in data for decision making (Ngai, Hu, Wong, Chen, & Sun, 2011). Historical data, i.e., financial data or time series data of stocks are readily available and huge in size. Applying data mining techniques on the data will definitely allow researchers in identifying and uncovering the hidden patterns of a particular stock or even a stock market. Classification, clustering and generalization are among the commonly used data mining techniques to analyse and predict the movement of stock prices or stock market indexes. In this research, clustering algorithms were adopted on the financial data of plantation stocks listed on Bursa Malaysia. Clustering in the data mining context refers to unsupervised classification of data into clusters/groups, and the data in the same cluster exhibit a certain degree of pattern similarity (Jain, Murty, & Flynn, 1999). The clustering algorithms have been widely used in many disciplines such as Bioinformatics (Ng, Ho, & Phon-Amnuaisuk, 2012), big data analytics (Feldman, Schmidt, & Sohler, 2013), multi-level Kohonen network learning (Shamsuddin, Zainal, & Mohd Yusof, 2008), etc.

    Although clustering research on stock market data is not new, but the research remains challenging. This is because the size of stock market data can be substantially huge and they often need to be pre-processed carefully and accurately before use. Furthermore, the patterns exist in the data of a particular stock market data might be different from others. Hence, the stock research with clustering is still intact and attractive to many researchers. In a study by Nanda, Mahanty, & Tiwari (2010), clustering was performed on stocks listed on Bombay Stock Exchange (BSE) with the objective of building a stock portfolio via the selection of stocks from the resulted clusters, and then compared the investment returns with the Sensex index; the research indicated that KM clustering yielded better results as compared to Self-organizing Map (SOM) and Fuzzy C-Means. Lee, Lin, Kao, & Chen (2010) applied hierarchical agglomerative and KM clustering to predict the short-term movement of stock prices after releasing the financial reports.

    Clustering technique was also applied to predict and assess the stock market co-movement (Aghabozorgi & Teh, 2014). The researchers proposed a three-phase clustering method to group the stocks listed on the Kuala Lumpur Stock Exchange (now known as Bursa Malaysia). It started the first phase with the approximate clustering of the stocks using a low-resolution time series data. The clusters formed were further refined by splitting them into sub-clusters in

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    Journal of ICT, 15, No. 2 (December) 2016, pp: 6384

    the second phase. The third phase involved the merging of sub-clusters into the final clusters. Hsu (2011) proposed a hybrid method to predict the prices of stocks listed on Taiwan Stock Exchange; the hybrid method was formed by integrating the SOM and genetic programming. The researcher claimed that the hybrid method was effective for stock price prediction.

    Classification technique was also employed in the stock market research. A fuzzy rule based system was proposed by Chang and Liu (2008) to predict the electronic stock prices in Taiwan Stock Exchange. Besides, Tan, Yong, & Tay (2012) applied Bayesian Networks (BN) to model the financial ratios of plantation stocks listed in Malaysia; the developed model can be used to forecast the future price performance of the plantation stocks. Much early research employed Artificial Neural Network (ANN) to predict the stock market. Thus, there were quite a number of ANN-based stock prediction models reported by financial researchers (Zhang & Wu, 2009; Ishikawa, Fukuhara, & Nakamura, 1997; Yoon & Swales, 1991).

    Data mining research in stock markets generally uses (1) fundamental analysis or (2) technical analysis to analyse stocks (Lam, 2004). Fundamental analysis refers to the finding of the intrinsic value of a stock that can be measured from the stocks quantitative and qualitative data (Tan et al., 2012; Lee et al., 2010; Nanda et al., 2010; Yoon & Swales, 1991). Quantitative data mainly comprise of financial ratios such as profit margin, debt ratio, price earnings ratio, etc. Qualitative data, on the other hand, link to the quality of key management, company policy, brand, marketing strategy, etc. Unlike fundamental analysis, technical analysis emphasises on the patterns and trends of a stock trading information; it gathers and analyses statistics generated by stock activities, i.e., price movement and volume. The patterns or trends discovered by the technical analysis are used as indicators to predict future stock price performance (Aghabozorgi & Teh, 2014; Hsu, 2011; Zhang & Wu, 2009; Chang & Liu, 2008). These two analyses produced relevant stock information that is beneficial to investors in building stock portfolios.

    A stock portfolio is a collection of stocks possessed by an individual or a company. Building a stock portfolio often needs Herculean efforts from an investor. This is because of the large number of stocks in a stock market. For instance, there are more than 900 common stocks (excluding financial derivatives) listed on Bursa Malaysia. Thus, building a good stock portfolio is always not an easy task for an amateur or even a professional fund manager. It is always important for a stock investor to find an efficient way to build a good stock portfolio which can generate excellent investment returns.

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    Thus, the primary objective of this study is to perform a rapid profiling on the 38 plantation stocks listed on Bursa Malaysia using quantitative data of stocks and clustering algorithms. Selecting the plantation stocks for this study is mainly because these stocks play an important role in the economy of Malaysia. Besides, Malaysia is also among the world largest exporters of palm oil (Sulaiman, Abdullah, Gerhauser, & Shariff, 2011) and rubber (Nambiar, 2010).

    The organization of this paper is as follows. The second section provides the detail of the research methodology. The third section covers the clustering results for these three clustering algorithms, as well as the analysis and discussion of the results. The last section concludes the paper and suggests the future directions of this research.

    METHODOLOGY

    The overview of the methods used in this study is depicted in Figure 1. It was started with the collection of raw financial data for the plantation stocks listed on Bursa Malaysia. In the subsequent step, the collected data were transformed into useful financial ratios. It was then followed by clustering the stocks data using three clus